{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data \n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "tf.logging.set_verbosity(tf.logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Extracting ./mnist/train-images-idx3-ubyte.gz\nExtracting ./mnist/train-labels-idx1-ubyte.gz\nExtracting ./mnist/t10k-images-idx3-ubyte.gz\nExtracting ./mnist/t10k-labels-idx1-ubyte.gz\n(55000, 784)\n(55000,)\n(5000, 784)\n(5000,)\n(10000, 784)\n(10000,)\n"
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\"./mnist\")\n",
    "\n",
    "print(mnist.train.images.shape)\n",
    "print(mnist.train.labels.shape)\n",
    "\n",
    "print(mnist.validation.images.shape)\n",
    "print(mnist.validation.labels.shape)\n",
    "\n",
    "print(mnist.test.images.shape)\n",
    "print(mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 576x576 with 16 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4,4, idx+1)\n",
    "    plt.axis('off')\n",
    "    plt.title('[{}]'.format(mnist.train.labels[idx]))\n",
    "    plt.imshow(mnist.train.images[idx].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Tensor(\"Softmax_6:0\", shape=(?, 10), dtype=float32)\nTensor(\"Equal_6:0\", shape=(?,), dtype=bool)\nTensor(\"Mean_13:0\", shape=(), dtype=float32)\nafter 100 training steps, the loss is 0.386755, the validation accuracy is 0.8732\nafter 200 training steps, the loss is 0.269087, the validation accuracy is 0.9146\nafter 300 training steps, the loss is 0.122861, the validation accuracy is 0.9154\nafter 400 training steps, the loss is 0.205006, the validation accuracy is 0.9352\nafter 500 training steps, the loss is 0.451995, the validation accuracy is 0.9322\nafter 600 training steps, the loss is 0.211898, the validation accuracy is 0.9236\nafter 700 training steps, the loss is 0.148794, the validation accuracy is 0.9454\nafter 800 training steps, the loss is 0.0694991, the validation accuracy is 0.9488\nafter 900 training steps, the loss is 0.255187, the validation accuracy is 0.952\nthe training is finish!\nthe test accuarcy is: 0.9462\n"
    }
   ],
   "source": [
    "x = tf.placeholder(\"float\", [None, 784])\n",
    "y = tf.placeholder(\"int64\", [None])\n",
    "learning_rate = tf.placeholder(\"float\")\n",
    "def initialize(shape, stddev=0.1):\n",
    "  return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "L1_units_count = 100\n",
    "\n",
    "W_1 = tf.Variable(initialize([784, L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(x, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count = 10 \n",
    "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2  \n",
    "\n",
    "logits = logits_2\n",
    "\n",
    "cross_entropy_loss = tf.reduce_mean(\n",
    "    tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(\n",
    "    learning_rate=learning_rate).minimize(cross_entropy_loss)\n",
    "\n",
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.argmax(pred, 1), y)\n",
    "print(pred)\n",
    "print(correct_pred)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "print(accuracy)\n",
    "batch_size = 32\n",
    "trainig_step = 1000\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    #定义验证集与测试集\n",
    "    validate_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels,\n",
    "    }\n",
    "    test_data = {x: mnist.test.images, y: mnist.test.labels}\n",
    "\n",
    "    for i in range(trainig_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss = sess.run(\n",
    "            [optimizer, cross_entropy_loss],\n",
    "            feed_dict={\n",
    "                x: xs,\n",
    "                y: ys,\n",
    "                learning_rate: 0.3\n",
    "            })\n",
    "        #每100次训练打印一次损失值与验证准确率\n",
    "        if i > 0 and i % 100 == 0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict=validate_data)\n",
    "            print(\n",
    "                \"after %d training steps, the loss is %g, the validation accuracy is %g\"\n",
    "                % (i, loss, validate_accuracy))\n",
    "            saver.save(sess, './model.ckpt', global_step=i)\n",
    "\n",
    "    print(\"the training is finish!\")\n",
    "    #最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict=test_data)\n",
    "    print(\"the test accuarcy is:\", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "INFO:tensorflow:Restoring parameters from ./model.ckpt-900\n0.9375\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 576x576 with 16 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('./')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict={\n",
    "                x: mnist.test.images[:16],\n",
    "                y: mnist.test.labels[:16]\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        print(acc)\n",
    "        for idx in range(16):\n",
    "            order = orders[idx, :][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(4, 4, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('{}: [{}]-[{:.1f}%]'.format(mnist.test.labels[idx],\n",
    "                                                  order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))\n",
    "\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}